Detecting Scam Tokens and Backdoor Functions in EVM Based Networks

Sergio Anguita Lorenzo; Jose Miguel-Alonso; Aitor Gomez-Goiri

doi:10.1007/978-3-031-81928-5_28

Detecting Scam Tokens and Backdoor Functions in EVM Based Networks

Sergio Anguita Lorenzo^*
, Jose Miguel-Alonso
, Aitor Gomez-Goiri

^*Corresponding author for this work

CIBERSEC and DLT

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The blockchain ecosystem is currently becoming an intricate landscape given the large number of attacks, scams, and hacks that occur. With such risk surrounding Blockchain DApps and users, we propose a method to help in the earlier detection of scam tokens. While blockchain technology continues to grow in multiple industries, the proliferation of fraudulent activities in the decentralized ecosystem has become a significant threat to the well-being and trustworthiness of the whole ecosystem. This scam tokens are deployed in plain sight in decentralized networks and listed in platforms such as CoinMarketCap and other token issuance platforms. In this paper, we identify over 1450 unreported scam tokens, 167 different backdoor functions, and 1428 malicious contract creators. By increasing the collaboration between industry stakeholders and web3 companies, our scam tokens dataset and detection tools can help in web3 threat detection while creating a more secure and resilient blockchain environment, fostering trust and innovation in the decentralized space.

Original language	English
Title of host publication	Blockchain and Applications, 6th International Congress
Editors	Javier Prieto, Rafael Pastor Vargas, Oscar Lage, José Manuel Machado, Molnár Bálint
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	288-298
Number of pages	11
ISBN (Print)	9783031819278
DOIs	https://doi.org/10.1007/978-3-031-81928-5_28
Publication status	Published - 2025
Event	6th International Congress on Blockchain and Applications, BLOCKCHAIN 2024 - Salamanca, Spain Duration: 26 Jun 2024 → 28 Jun 2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1256 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	6th International Congress on Blockchain and Applications, BLOCKCHAIN 2024
Country/Territory	Spain
City	Salamanca
Period	26/06/24 → 28/06/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Blockchain
EVM
Phishing-detection
Scam

Access to Document

10.1007/978-3-031-81928-5_28

Cite this

Lorenzo, S. A., Miguel-Alonso, J., & Gomez-Goiri, A. (2025). Detecting Scam Tokens and Backdoor Functions in EVM Based Networks. In J. Prieto, R. P. Vargas, O. Lage, J. M. Machado, & M. Bálint (Eds.), Blockchain and Applications, 6th International Congress (pp. 288-298). (Lecture Notes in Networks and Systems; Vol. 1256 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-81928-5_28

Lorenzo, Sergio Anguita ; Miguel-Alonso, Jose ; Gomez-Goiri, Aitor. / Detecting Scam Tokens and Backdoor Functions in EVM Based Networks. Blockchain and Applications, 6th International Congress. editor / Javier Prieto ; Rafael Pastor Vargas ; Oscar Lage ; José Manuel Machado ; Molnár Bálint. Springer Science and Business Media Deutschland GmbH, 2025. pp. 288-298 (Lecture Notes in Networks and Systems).

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abstract = "The blockchain ecosystem is currently becoming an intricate landscape given the large number of attacks, scams, and hacks that occur. With such risk surrounding Blockchain DApps and users, we propose a method to help in the earlier detection of scam tokens. While blockchain technology continues to grow in multiple industries, the proliferation of fraudulent activities in the decentralized ecosystem has become a significant threat to the well-being and trustworthiness of the whole ecosystem. This scam tokens are deployed in plain sight in decentralized networks and listed in platforms such as CoinMarketCap and other token issuance platforms. In this paper, we identify over 1450 unreported scam tokens, 167 different backdoor functions, and 1428 malicious contract creators. By increasing the collaboration between industry stakeholders and web3 companies, our scam tokens dataset and detection tools can help in web3 threat detection while creating a more secure and resilient blockchain environment, fostering trust and innovation in the decentralized space.",

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Lorenzo, SA, Miguel-Alonso, J & Gomez-Goiri, A 2025, Detecting Scam Tokens and Backdoor Functions in EVM Based Networks. in J Prieto, RP Vargas, O Lage, JM Machado & M Bálint (eds), Blockchain and Applications, 6th International Congress. Lecture Notes in Networks and Systems, vol. 1256 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 288-298, 6th International Congress on Blockchain and Applications, BLOCKCHAIN 2024, Salamanca, Spain, 26/06/24. https://doi.org/10.1007/978-3-031-81928-5_28

Detecting Scam Tokens and Backdoor Functions in EVM Based Networks. / Lorenzo, Sergio Anguita; Miguel-Alonso, Jose; Gomez-Goiri, Aitor.
Blockchain and Applications, 6th International Congress. ed. / Javier Prieto; Rafael Pastor Vargas; Oscar Lage; José Manuel Machado; Molnár Bálint. Springer Science and Business Media Deutschland GmbH, 2025. p. 288-298 (Lecture Notes in Networks and Systems; Vol. 1256 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2025

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N2 - The blockchain ecosystem is currently becoming an intricate landscape given the large number of attacks, scams, and hacks that occur. With such risk surrounding Blockchain DApps and users, we propose a method to help in the earlier detection of scam tokens. While blockchain technology continues to grow in multiple industries, the proliferation of fraudulent activities in the decentralized ecosystem has become a significant threat to the well-being and trustworthiness of the whole ecosystem. This scam tokens are deployed in plain sight in decentralized networks and listed in platforms such as CoinMarketCap and other token issuance platforms. In this paper, we identify over 1450 unreported scam tokens, 167 different backdoor functions, and 1428 malicious contract creators. By increasing the collaboration between industry stakeholders and web3 companies, our scam tokens dataset and detection tools can help in web3 threat detection while creating a more secure and resilient blockchain environment, fostering trust and innovation in the decentralized space.

AB - The blockchain ecosystem is currently becoming an intricate landscape given the large number of attacks, scams, and hacks that occur. With such risk surrounding Blockchain DApps and users, we propose a method to help in the earlier detection of scam tokens. While blockchain technology continues to grow in multiple industries, the proliferation of fraudulent activities in the decentralized ecosystem has become a significant threat to the well-being and trustworthiness of the whole ecosystem. This scam tokens are deployed in plain sight in decentralized networks and listed in platforms such as CoinMarketCap and other token issuance platforms. In this paper, we identify over 1450 unreported scam tokens, 167 different backdoor functions, and 1428 malicious contract creators. By increasing the collaboration between industry stakeholders and web3 companies, our scam tokens dataset and detection tools can help in web3 threat detection while creating a more secure and resilient blockchain environment, fostering trust and innovation in the decentralized space.

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T2 - 6th International Congress on Blockchain and Applications, BLOCKCHAIN 2024

Y2 - 26 June 2024 through 28 June 2024

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Lorenzo SA, Miguel-Alonso J, Gomez-Goiri A. Detecting Scam Tokens and Backdoor Functions in EVM Based Networks. In Prieto J, Vargas RP, Lage O, Machado JM, Bálint M, editors, Blockchain and Applications, 6th International Congress. Springer Science and Business Media Deutschland GmbH. 2025. p. 288-298. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-81928-5_28